油罐扬沸火灾预测方法的研究及安全预警系统的建立

通过研究扬沸前兆噪声特性及其声发射机理，并与典型环境噪声特性差异作了分 析、比较，提取出一组识别特征物理量，以便在扬沸火灾早期能诊断预报扬沸的发生。     

矿井突水预测的多源信息方法应用研究

在对国内外近年应用地理信息系统（GIS）解决地质学问题进行简要评述的基础上，通过实例阐述了借助于GIS技术，采用多源信息方法进行煤矿区突水预测的方法与步骤及预测结果。并展望了该方法在矿井突水预测中的应用前景。     

A new method for predicting vegetation distributions using decision tree analysis in a geographic information system

Decision tree analysis was used to predict the distribution of forest communities in an area on the south coast of New South Wales, Australia. The analysis was carried out using a geographical information system environmental data base of those topographic and geological variables thought to influence the distribution of vegetation and derived from cartographic sources. The resulting maps of forest communities are of a resolution sufficient to delimit individual forest stands and contain much ecological information.     

矿井瓦斯涌出量预测的灰色建模法

简要介绍了瓦斯涌出量的常用预测方法 ,指出了各种预测方法的弊端 ,从矿山实际出发 ,把非等间距数列变为等间距数列 ,根据灰色理论提出的预测方法 ,利用不同采深瓦斯涌出量的原始数据建立矿井瓦斯涌出量的动态GM(1,1)模型 ,进行瓦斯涌出量预测 ,选择了合理的误差检验模型 ,并通过实例说明了GM(1,1)模型在预测瓦斯涌出量中的应用 ,结果表明预测程度较高。对矿井延深做好瓦斯涌出量预测并进行矿井安全生产具有很好的指导意义。     

TSP203隧道地震预报系统在拱坝隧道围岩稳定性超前预报中的应用

以上(海)瑞(丽)高速公路湖南境内邵(阳)-怀(化)段拱坝隧道掘进中掌子面前方岩体结构的超前预报为例,介绍了TSP203地球物理探测新方法,预报了该隧道右线YK91+163-YK91+080段的围岩由于节理发育,且地表水与节理裂隙贯通性较好,因此稳定性较差,在施工中将很易造成坍塌,应按Ⅱ类围岩支护;在YK91+080-YK91+045段围岩稳定性较好,可视具体情况变更支护方案;YK91+031-YK91+013段,可能存在层间破碎带或软弱夹层,在施工过程中需要加强支护。通过探测保证了及时、详细、准确地了解开挖掌子面前方的岩体结构情况,为施工单位合理安排施工作业进度、保障施工人员生命安全、减少工程隐患提供了科学依据。     

太湖叶绿素a浓度预测模型初探

以太湖2005年的监测资料为基础,运用多元统计回归和BP人工神经网络方法构建模型,探求叶绿素a与水深、水温、营养盐等10项环境因子之间的关系,通过验证发现BP模型对叶绿素a浓度的拟合值与叶绿素a浓度的实测值之间的均方误差为220．3059,优于统计回归模型的235．4569;此外对两种模型进行了灵敏度测试,结果都显示总磷不是太湖叶绿素a浓度的限制因子,而水深、水温、总氮的变化对叶绿素a浓度影响显著。本研究对太湖叶绿素a浓度预测模型的建立是十分有意义的。     

长春南湖水质分析评价与污染趋势预测

本文剃用内梅罗污染指数法时2000至2010年期间长春南湖水环境质量进行了综合分析评价,选取将氨氮、硝酸盐氮、亚硝酸盐氮、总氮、总磷、COD和BOD,作为水环境评价的主要参评参数。结果表明自2000至今南湖水体内梅罗综合指数呈现增加趋势,南湖水体污染等级处于中度污染水平,并有再次爆发水华现象的可能,因此应加大南湖水体污...     

Assessing BMP Effectiveness and Guiding BMP Planning Using Process‐Based Modeling

There is an increasing need for improved process‐based planning tools to assist watershed managers in the selection and placement of effective best management practices (BMPs). In this article, we present an approach, based on the Water Erosion Prediction Project model and a pesticide transport model, to identify dominant hydrologic flow paths and critical source areas for a variety of pollutant types. We use this approach to compare the relative impacts of BMPs on hydrology, erosion, sediment, and pollutant delivery within different landscapes. Specifically, we focus on using this approach to understand what factors promoted and/or hindered BMP effectiveness at three Conservation Effects Assessment Project watersheds: Paradise Creek Watershed in Idaho, Walnut Creek Watershed in Iowa, and Goodwater Creek Experimental Watershed in Missouri. These watersheds were first broken down into unique land types based on soil and topographic characteristics. We used the model to assess BMP effectiveness in each of these land types. This simple process‐based modeling approach provided valuable insights that are not generally available to planners when selecting and locating BMPs and helped explain fundamental reasons why long‐term improvement in water quality of these three watersheds has yet to be completely realized.     

A new method for the prediction of flash points for ternary miscible mixtures

The flash point is one of the most important physicochemical parameters used to characterize the fire and explosion hazard for flammable liquids. The flash points of ternary miscible mixtures with different components and compositions were measured in this study. Four model input parameters, being normal boiling point, the standard enthalpy of vaporization, the average number of carbon atoms and the stoichiometric concentration of the gas phase for mixtures, were employed and calculated based on the theory of vapor–liquid equilibrium. Both multiple linear regression (MLR) and multiple nonlinear regression (MNR) methods were applied to develop prediction models for the flash points of ternary miscible mixtures. The developed predictive models were validated using data measured experimentally as well as taking data on flash points of ternairy mixtures from the literature. Results showed that the obtained average absolute error of both the MLR and the MNR model for all the datasets were within the range of experimental error of flash point measurements. It is shown that the presented models can be effectively used to predict the flash points of ternary mixtures with only some common physicochemical parameters.     

Prediction of effluent concentration in a wastewater treatment plant using machine learning models

Of growing amount of food waste, the integrated food waste and waste water treatment was regarded as one of the efficient modeling method. However, the load of food waste to the conventional waste treatment process might lead to the high concentration of total nitrogen (T-N) impact on the effluent water quality. The objective of this study is to establish two machine learning models—artificial neural networks (ANNs) and support vector machines (SVMs), in order to predict 1-day interval T-N concentration of effluent from a wastewater treatment plant in Ulsan, Korea. Daily water quality data and meteorological data were used and the performance of both models was evaluated in terms of the coefficient of determination (R²), Nash–Sutcliff efficiency (NSE), relative efficiency criteria (d_rel). Additionally, Latin-Hypercube one-factor-at-a-time (LH-OAT) and a pattern search algorithm were applied to sensitivity analysis and model parameter optimization, respectively. Results showed that both models could be effectively applied to the 1-day interval prediction of T-N concentration of effluent. SVM model showed a higher prediction accuracy in the training stage and similar result in the validation stage. However, the sensitivity analysis demonstrated that the ANN model was a superior model for 1-day interval T-N concentration prediction in terms of the cause-and-effect relationship between T-N concentration and modeling input values to integrated food waste and waste water treatment. This study suggested the efficient and robust nonlinear time-series modeling method for an early prediction of the water quality of integrated food waste and waste water treatment process.